On the Design of Polycrystalline Materials with an Integration of Multiscale Modeling and Statistical Learning

多尺度建模与统计学习相结合的多晶材料设计

• 批准号: 0757824
• 负责人: Nicholas Zabaras
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-15 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757824&HistoricalAwards=false
• 关键词: Design; Polycrystalline; Materials; Integration; Multiscale

The research project provides a new outlook to materials design problems by integrating microstructural models with virtual databases and statistical learning tools. In particular, emphasis is given to (1) Development of innovative computational techniques for microstructure interrogation and multi-scaling of micro-scale deformation and failure, (2) Development of hierarchical microstructure libraries of microstructural signatures (crystal orientations and grain size distributions) with associations to properties (such as strength, toughness and formability) using statistical learning algorithms, (3) Design techniques for identification of the best set of initial features in the microstructure in particular applications, (4) Statistical tools for real-time selection of thermo-mechanical processing sequences to customize microstructures for achieving desired property distributions, and (5) Performing multi-scale finite element analysis for testing and experimentally verifying microstructure design solutions in complex engineering applications. The proposed multi-scale design framework can lead to significant reduction of computational overhead in materials design, thus allowing accelerated insertion of materials and materials processes.While these techniques and libraries will be implemented for polycrystalline FCC aluminum alloys, they will also impact microstructure-sensitive design of many other polycrystalline systems. Improved methods for materials and process design would produce far-reaching benefits to the materials industry and economy. The proposed developments are not specific to materials design; if successful, they could be applied to other complicated design problems in a variety of scientific fields. Dissemination of the algorithms and software tools will enable broad research communities to harness databases of individual length scales and to infer how structures and properties are linked. The problems addressed provide a unique and valuable opportunity for undergraduate and graduate students to work in a multidisciplinary environment that emphasizes the significant and growing roles of multiscale modeling and statistical learning in materials development and design.

该研究项目通过将微观结构模型与虚拟数据库和统计学习工具相结合，为材料设计问题提供了新的前景。 重点是：（1）发展微结构查询和多尺度微结构变形与破坏的计算技术;（2）发展微结构特征的分级微结构库（晶体取向和粒度分布）与性能的关系（如强度、韧性和可成形性），（3）用于在特定应用中识别微结构中的最佳初始特征集的设计技术，（4）用于实时选择热机械处理序列的统计工具，以定制用于实现所需属性分布的微结构，以及（5）执行多尺度有限元分析，以测试和实验验证复杂工程应用中的微结构设计解决方案。提出的多尺度设计框架可以显着减少材料设计中的计算开销，从而加速材料和材料工艺的插入。虽然这些技术和库将用于多晶FCC铝合金，但它们也将影响微结构敏感设计许多其他多晶系统。材料和工艺设计方法的改进将对材料工业和经济产生深远的影响。所提出的发展并不局限于材料设计;如果成功，它们可以应用于各种科学领域的其他复杂设计问题。算法和软件工具的传播将使广泛的研究社区能够利用个人长度尺度的数据库，并推断结构和属性是如何联系在一起的。 解决的问题提供了一个独特的和宝贵的机会，本科生和研究生在多学科的环境中工作，强调多尺度建模和统计学习在材料开发和设计中的重要和日益增长的作用。